⚔🛡 Awesome Graph Adversarial Learning

• ⚔🛡 Awesome Graph Adversarial Learning
• 👀Quick Look
• ⚔Attack
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
• 🛡Defense
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
• 🔐Certification
• ⚖Stability
• 🚀Others
• 📃Survey
• ⚙Toolbox
• 🔗Resource

This repository contains Attack-related papers, Defense-related papers, Robustness Certification papers, etc., ranging from 2017 to 2021. If you find this repo useful, please cite: A Survey of Adversarial Learning on Graph, arXiv'20, Link

@article{chen2020survey,
  title={A Survey of Adversarial Learning on Graph},
  author={Chen, Liang and Li, Jintang and Peng, Jiaying and Xie, 
        Tao and Cao, Zengxu and Xu, Kun and He, 
        Xiangnan and Zheng, Zibin and Wu, Bingzhe},
  journal={arXiv preprint arXiv:2003.05730},
  year={2020}
}

👀Quick Look

The papers in this repo are categorized or sorted:

| By Alphabet | By Year | By Venue | Papers with Code |

If you want to get a quick look at the recently updated papers in the repository (in 30 days), you can refer to 📍this.

⚔Attack

2022

💨 Back to Top

• Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem, 📝WSDM, :octocat:Code
• Inference Attacks Against Graph Neural Networks, 📝USENIX Security, :octocat:Code
• Model Stealing Attacks Against Inductive Graph Neural Networks, 📝IEEE Symposium on Security and Privacy, :octocat:Code
• Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, 📝WWW, :octocat:Code
• Neighboring Backdoor Attacks on Graph Convolutional Network, 📝arXiv, :octocat:Code
• Understanding and Improving Graph Injection Attack by Promoting Unnoticeability, 📝ICLR, :octocat:Code
• Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs, 📝AAAI, :octocat:Code
• More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks, 📝arXiv
• Black-box Node Injection Attack for Graph Neural Networks, 📝arXiv, :octocat:Code
• Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection, 📝arXiv
• Projective Ranking-based GNN Evasion Attacks, 📝arXiv
• GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation, 📝arXiv
• Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization, 📝Asia CCS, :octocat:Code
• Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees, 📝CVPR, :octocat:Code
• Transferable Graph Backdoor Attack, 📝RAID, :octocat:Code
• Adversarial Robustness of Graph-based Anomaly Detection, 📝arXiv
• Label specificity attack: Change your label as I want, 📝IJIS
• AdverSparse: An Adversarial Attack Framework for Deep Spatial-Temporal Graph Neural Networks, 📝ICASSP
• Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks, 📝WSDM
• Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors, 📝IJCAI, 📝:octocat:Code
• Label-Only Membership Inference Attack against Node-Level Graph Neural NetworksCluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors, 📝arXiv
• Adversarial Camouflage for Node Injection Attack on Graphs, 📝arXiv
• Are Gradients on Graph Structure Reliable in Gray-box Attacks?, 📝CIKM, 📝:octocat:Code
• Adversarial Camouflage for Node Injection Attack on Graphs, 📝arXiv
• Graph Structural Attack by Perturbing Spectral Distance, 📝aKDD

2021

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• Stealing Links from Graph Neural Networks, 📝USENIX Security
• PATHATTACK: Attacking Shortest Paths in Complex Networks, 📝arXiv
• Structack: Structure-based Adversarial Attacks on Graph Neural Networks, 📝ACM Hypertext, :octocat:Code
• Optimal Edge Weight Perturbations to Attack Shortest Paths, 📝arXiv
• GReady for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack, 📝Information Sciences
• Graph Adversarial Attack via Rewiring, 📝KDD, :octocat:Code
• Membership Inference Attack on Graph Neural Networks, 📝arXiv
• BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection, 📝arXiv
• Graph Backdoor, 📝USENIX Security
• TDGIA: Effective Injection Attacks on Graph Neural Networks, 📝KDD, :octocat:Code
• Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge, 📝arXiv
• Adversarial Attack on Large Scale Graph, 📝TKDE, :octocat:Code
• Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense, 📝arXiv
• Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks, 📝arXiv
• Universal Spectral Adversarial Attacks for Deformable Shapes, 📝CVPR
• SAGE: Intrusion Alert-driven Attack Graph Extractor, 📝KDD Workshop, :octocat:Code
• Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models, 📝arXiv, :octocat:Code
• VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning, 📝PAKDD, :octocat:Code
• Explainability-based Backdoor Attacks Against Graph Neural Networks, 📝WiseML@WiSec
• GraphAttacker: A General Multi-Task GraphAttack Framework, 📝arXiv, :octocat:Code
• Attacking Graph Neural Networks at Scale, 📝AAAI workshop
• Node-Level Membership Inference Attacks Against Graph Neural Networks, 📝arXiv
• Reinforcement Learning For Data Poisoning on Graph Neural Networks, 📝arXiv
• DeHiB: Deep Hidden Backdoor Attack on Semi-Supervised Learning via Adversarial Perturbation, 📝AAAI
• Graphfool: Targeted Label Adversarial Attack on Graph Embedding, 📝arXiv
• Towards Revealing Parallel Adversarial Attack on Politician Socialnet of Graph Structure, 📝Security and Communication Networks
• Network Embedding Attack: An Euclidean Distance Based Method, 📝MDATA
• Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation, 📝arXiv
• Jointly Attacking Graph Neural Network and its Explanations, 📝arXiv
• Graph Stochastic Neural Networks for Semi-supervised Learning, 📝arXiv, :octocat:Code
• Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings, 📝arXiv, :octocat:Code
• Single-Node Attack for Fooling Graph Neural Networks, 📝KDD Workshop, :octocat:Code
• The Robustness of Graph k-shell Structure under Adversarial Attacks, 📝arXiv
• Poisoning Knowledge Graph Embeddings via Relation Inference Patterns, 📝ACL, :octocat:Code
• A Hard Label Black-box Adversarial Attack Against Graph Neural Networks, 📝CCS
• GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking, 📝DATE Conference
• Single Node Injection Attack against Graph Neural Networks, 📝CIKM, :octocat:Code
• Spatially Focused Attack against Spatiotemporal Graph Neural Networks, 📝arXiv
• Derivative-free optimization adversarial attacks for graph convolutional networks, 📝PeerJ
• Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks, 📝CIKM
• Time-aware Gradient Attack on Dynamic Network Link Prediction, 📝TKDE
• Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning, 📝arXiv
• Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications, 📝ICDM, :octocat:Code
• Watermarking Graph Neural Networks based on Backdoor Attacks, 📝arXiv
• Robustness of Graph Neural Networks at Scale, 📝NeurIPS, :octocat:Code
• Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness, 📝NeurIPS
• Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models, 📝IJCAI, :octocat:Code
• Adversarial Attacks on Graph Classification via Bayesian Optimisation, 📝NeurIPS, :octocat:Code
• Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods, 📝EMNLP, :octocat:Code
• COREATTACK: Breaking Up the Core Structure of Graphs, 📝arXiv
• UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction, 📝ICCAD, :octocat:Code
• GraphMI: Extracting Private Graph Data from Graph Neural Networks, 📝IJCAI, :octocat:Code
• Structural Attack against Graph Based Android Malware Detection, 📝CCS
• Adversarial Attack against Cross-lingual Knowledge Graph Alignment, 📝EMNLP
• FHA: Fast Heuristic Attack Against Graph Convolutional Networks, 📝ICDS
• Task and Model Agnostic Adversarial Attack on Graph Neural Networks, 📝arXiv
• How Members of Covert Networks Conceal the Identities of Their Leaders, 📝ACM TIST
• Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification, 📝arXiv

2020

💨 Back to Top

• A Graph Matching Attack on Privacy-Preserving Record Linkage, 📝CIKM
• Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection, 📝arXiv
• Adaptive Adversarial Attack on Graph Embedding via GAN, 📝SocialSec
• Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers, 📝arXiv
• One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting, 📝ICLR OpenReview
• Near-Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem, 📝ICLR OpenReview
• Adversarial Attacks on Deep Graph Matching, 📝NeurIPS
• Attacking Graph-Based Classification without Changing Existing Connections, 📝ACSAC
• Cross Entropy Attack on Deep Graph Infomax, 📝IEEE ISCAS
• Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation, 📝ICLR, :octocat:Code
• Towards More Practical Adversarial Attacks on Graph Neural Networks, 📝NeurIPS, :octocat:Code
• Adversarial Label-Flipping Attack and Defense for Graph Neural Networks, 📝ICDM, :octocat:Code
• Exploratory Adversarial Attacks on Graph Neural Networks, 📝ICDM, :octocat:Code
• A Targeted Universal Attack on Graph Convolutional Network, 📝arXiv, :octocat:Code
• Query-free Black-box Adversarial Attacks on Graphs, 📝arXiv
• Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs, 📝arXiv
• Efficient Evasion Attacks to Graph Neural Networks via Influence Function, 📝arXiv
• Backdoor Attacks to Graph Neural Networks, 📝SACMAT, :octocat:Code
• Link Prediction Adversarial Attack Via Iterative Gradient Attack, 📝IEEE Trans
• Adversarial Attack on Hierarchical Graph Pooling Neural Networks, 📝arXiv
• Adversarial Attack on Community Detection by Hiding Individuals, 📝WWW, :octocat:Code
• Manipulating Node Similarity Measures in Networks, 📝AAMAS
• A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models, 📝AAAI, :octocat:Code
• Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks, 📝BigData
• Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach, 📝WWW
• An Efficient Adversarial Attack on Graph Structured Data, 📝IJCAI Workshop
• Practical Adversarial Attacks on Graph Neural Networks, 📝ICML Workshop
• Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns, 📝TKDD
• Adversarial Attacks on Link Prediction Algorithms Based on Graph Neural Networks, 📝Asia CCS
• Scalable Attack on Graph Data by Injecting Vicious Nodes, 📝ECML-PKDD, :octocat:Code
• Attackability Characterization of Adversarial Evasion Attack on Discrete Data, 📝KDD
• MGA: Momentum Gradient Attack on Network, 📝arXiv
• Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria, 📝arXiv
• Adversarial Perturbations of Opinion Dynamics in Networks, 📝arXiv
• Network disruption: maximizing disagreement and polarization in social networks, 📝arXiv, :octocat:Code
• Adversarial attack on BC classification for scale-free networks, 📝AIP Chaos

2019

💨 Back to Top

• Attacking Graph Convolutional Networks via Rewiring, 📝arXiv
• Unsupervised Euclidean Distance Attack on Network Embedding, 📝arXiv
• Structured Adversarial Attack Towards General Implementation and Better Interpretability, 📝ICLR, :octocat:Code
• Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling, 📝arXiv
• Vertex Nomination, Consistent Estimation, and Adversarial Modification, 📝arXiv
• PeerNets Exploiting Peer Wisdom Against Adversarial Attacks, 📝ICLR, :octocat:Code
• Network Structural Vulnerability A Multi-Objective Attacker Perspective, 📝IEEE Trans
• Multiscale Evolutionary Perturbation Attack on Community Detection, 📝arXiv
• αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model, 📝CIKM
• Adversarial Attacks on Node Embeddings via Graph Poisoning, 📝ICML, :octocat:Code
• GA Based Q-Attack on Community Detection, 📝TCSS
• Data Poisoning Attack against Knowledge Graph Embedding, 📝IJCAI
• Adversarial Attacks on Graph Neural Networks via Meta Learning, 📝ICLR, :octocat:Code
• Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective, 📝IJCAI, :octocat:Code
• Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, 📝IJCAI, :octocat:Code
• A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning, 📝NeurIPS, :octocat:Code
• Attacking Graph-based Classification via Manipulating the Graph Structure, 📝CCS

2018

💨 Back to Top

• Fake Node Attacks on Graph Convolutional Networks, 📝arXiv
• Data Poisoning Attack against Unsupervised Node Embedding Methods, 📝arXiv
• Fast Gradient Attack on Network Embedding, 📝arXiv
• Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network, 📝arXiv
• Adversarial Attacks on Neural Networks for Graph Data, 📝KDD, :octocat:Code
• Hiding Individuals and Communities in a Social Network, 📝Nature Human Behavior
• Attacking Similarity-Based Link Prediction in Social Networks, 📝AAMAS
• Adversarial Attack on Graph Structured Data, 📝ICML, :octocat:Code

2017

💨 Back to Top

• Practical Attacks Against Graph-based Clustering, 📝CCS
• Adversarial Sets for Regularising Neural Link Predictors, 📝UAI, :octocat:Code

🛡Defense

2022

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• Unsupervised Adversarially-Robust Representation Learning on Graphs, 📝AAAI, :octocat:Code
• Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels, 📝arXiv, :octocat:Code
• Mind Your Solver! On Adversarial Attack and Defense for Combinatorial Optimization, 📝arXiv, :octocat:Code
• Learning Robust Representation through Graph Adversarial Contrastive Learning, 📝arXiv
• GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks, 📝arXiv
• Graph Neural Network for Local Corruption Recovery, 📝arXiv, :octocat:Code
• Robust Heterogeneous Graph Neural Networks against Adversarial Attacks, 📝AAAI
• How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks?, 📝Neural Processing Letters
• Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision, 📝AAAI, :octocat:Code
• SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation, 📝WWW, :octocat:Code
• Exploring High-Order Structure for Robust Graph Structure Learning, 📝arXiv
• GUARD: Graph Universal Adversarial Defense, 📝arXiv, :octocat:Code
• Detecting Topology Attacks against Graph Neural Networks, 📝arXiv
• LPGNet: Link Private Graph Networks for Node Classification, 📝arXiv
• EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks, 📝arXiv
• Bayesian Robust Graph Contrastive Learning, 📝arXiv, :octocat:Code
• Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN, 📝KDD
• Robust Graph Representation Learning for Local Corruption Recovery, 📝ICML workshop
• Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond, 📝CVPR, :octocat:Code
• Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks, 📝arXiv
• Robust Graph Neural Networks via Ensemble Learning, 📝Mathematics
• AN-GCN: An Anonymous Graph Convolutional Network Against Edge-Perturbing Attacks, 📝IEEE TNNLS
• How does Heterophily Impact Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications, 📝KDD, :octocat:Code
• Robust Graph Neural Networks using Weighted Graph Laplacian, 📝SPCOM, :octocat:Code
• ARIEL: Adversarial Graph Contrastive Learning, 📝arXiv·
• Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation, 📝KDD, :octocat:Code
• NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs, 📝arXiv
• Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation, 📝CIKM, :octocat:Code

2021

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• Learning to Drop: Robust Graph Neural Network via Topological Denoising, 📝WSDM, :octocat:Code
• How effective are Graph Neural Networks in Fraud Detection for Network Data?, 📝arXiv
• Graph Sanitation with Application to Node Classification, 📝arXiv
• Understanding Structural Vulnerability in Graph Convolutional Networks, 📝IJCAI, :octocat:Code
• A Robust and Generalized Framework for Adversarial Graph Embedding, 📝arXiv, :octocat:Code
• Integrated Defense for Resilient Graph Matching, 📝ICML
• Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs, 📝ICASSP
• Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination, 📝WWW
• Information Obfuscation of Graph Neural Network, 📝ICML, :octocat:Code
• Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs, 📝arXiv
• On Generalization of Graph Autoencoders with Adversarial Training, 📝ECML
• DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs, 📝ECML
• Elastic Graph Neural Networks, 📝ICML, :octocat:Code
• Robust Counterfactual Explanations on Graph Neural Networks, 📝arXiv
• Node Similarity Preserving Graph Convolutional Networks, 📝WSDM, :octocat:Code
• Enhancing Robustness and Resilience of Multiplex Networks Against Node-Community Cascading Failures, 📝IEEE TSMC
• NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data, 📝TKDE, :octocat:Code
• Robust Graph Learning Under Wasserstein Uncertainty, 📝arXiv
• Towards Robust Graph Contrastive Learning, 📝arXiv
• Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks, 📝ICML
• UAG: Uncertainty-Aware Attention Graph Neural Network for Defending Adversarial Attacks, 📝AAAI
• Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks, 📝AAAI
• Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering, 📝AAAI, :octocat:Code
• Personalized privacy protection in social networks through adversarial modeling, 📝AAAI
• Interpretable Stability Bounds for Spectral Graph Filters, 📝arXiv
• Randomized Generation of Adversary-Aware Fake Knowledge Graphs to Combat Intellectual Property Theft, 📝AAAI
• Unified Robust Training for Graph NeuralNetworks against Label Noise, 📝arXiv
• An Introduction to Robust Graph Convolutional Networks, 📝arXiv
• E-GraphSAGE: A Graph Neural Network based Intrusion Detection System, 📝arXiv
• Spatio-Temporal Sparsification for General Robust Graph Convolution Networks, 📝arXiv
• Robust graph convolutional networks with directional graph adversarial training, 📝Applied Intelligence
• Detection and Defense of Topological Adversarial Attacks on Graphs, 📝AISTATS
• Unveiling the potential of Graph Neural Networks for robust Intrusion Detection, 📝arXiv, :octocat:Code
• Adversarial Robustness of Probabilistic Network Embedding for Link Prediction, 📝arXiv
• EGC2: Enhanced Graph Classification with Easy Graph Compression, 📝arXiv
• LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis, 📝arXiv
• Structure-Aware Hierarchical Graph Pooling using Information Bottleneck, 📝IJCNN
• Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights, 📝arXiv
• CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph, 📝arXiv
• Releasing Graph Neural Networks with Differential Privacy Guarantees, 📝arXiv
• Speedup Robust Graph Structure Learning with Low-Rank Information, 📝CIKM
• A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks, 📝ICICS, :octocat:Code
• Node Feature Kernels Increase Graph Convolutional Network Robustness, 📝arXiv, :octocat:Code
• On the Relationship between Heterophily and Robustness of Graph Neural Networks, 📝arXiv
• Distributionally Robust Semi-Supervised Learning Over Graphs, 📝ICLR
• Robustness of Graph Neural Networks at Scale, 📝NeurIPS, :octocat:Code
• Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation, 📝arXiv
• Not All Low-Pass Filters are Robust in Graph Convolutional Networks, 📝NeurIPS, :octocat:Code
• Towards Robust Reasoning over Knowledge Graphs, 📝arXiv
• Robust Graph Neural Networks via Probabilistic Lipschitz Constraints, 📝arXiv
• Graph Neural Networks with Adaptive Residual, NeurIPS, :octocat:Code
• Graph-based Adversarial Online Kernel Learning with Adaptive Embedding, 📝ICDM
• Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification, 📝NeurIPS, :octocat:Code
• Graph Neural Networks with Feature and Structure Aware Random Walk, 📝arXiv
• Topological Relational Learning on Graphs, 📝NeurIPS, :octocat:Code

2020

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• Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach, 📝ICLR OpenReview
• Provable Overlapping Community Detection in Weighted Graphs, 📝NeurIPS
• Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings, 📝NeurIPS, :octocat:Code
• Graph Random Neural Networks for Semi-Supervised Learning on Graphs, 📝NeurIPS, :octocat:Code
• Reliable Graph Neural Networks via Robust Aggregation, 📝NeurIPS, :octocat:Code
• Towards Robust Graph Neural Networks against Label Noise, 📝ICLR OpenReview
• Graph Adversarial Networks: Protecting Information against Adversarial Attacks, 📝ICLR OpenReview, :octocat:Code
• A Novel Defending Scheme for Graph-Based Classification Against Graph Structure Manipulating Attack, 📝SocialSec
• Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings, 📝NeurIPS, :octocat:Code
• Node Copying for Protection Against Graph Neural Network Topology Attacks, 📝arXiv
• Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian, 📝NeurIPS
• A Feature-Importance-Aware and Robust Aggregator for GCN, 📝CIKM, :octocat:Code
• Anti-perturbation of Online Social Networks by Graph Label Transition, 📝arXiv
• Graph Information Bottleneck, 📝NeurIPS, :octocat:Code
• Adversarial Detection on Graph Structured Data, 📝PPMLP
• Graph Contrastive Learning with Augmentations, 📝NeurIPS, :octocat:Code
• Learning Graph Embedding with Adversarial Training Methods, 📝IEEE Transactions on Cybernetics
• I-GCN: Robust Graph Convolutional Network via Influence Mechanism, 📝arXiv
• Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks, 📝AAAI
• Smoothing Adversarial Training for GNN, 📝IEEE TCSS
• Graph Structure Reshaping Against Adversarial Attacks on Graph Neural Networks, 📝None, :octocat:Code
• RoGAT: a robust GNN combined revised GAT with adjusted graphs, 📝arXiv
• ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks, 📝arXiv
• Adversarial Perturbations of Opinion Dynamics in Networks, 📝arXiv
• Adversarial Privacy Preserving Graph Embedding against Inference Attack, 📝arXiv, :octocat:Code
• Robust Graph Learning From Noisy Data, 📝IEEE Trans
• GNNGuard: Defending Graph Neural Networks against Adversarial Attacks, 📝NeurIPS, :octocat:Code
• Transferring Robustness for Graph Neural Network Against Poisoning Attacks, 📝WSDM, :octocat:Code
• All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs, 📝WSDM, :octocat:Code
• How Robust Are Graph Neural Networks to Structural Noise?, 📝DLGMA
• Robust Detection of Adaptive Spammers by Nash Reinforcement Learning, 📝KDD, :octocat:Code
• Graph Structure Learning for Robust Graph Neural Networks, 📝KDD, :octocat:Code
• On The Stability of Polynomial Spectral Graph Filters, 📝ICASSP, :octocat:Code
• On the Robustness of Cascade Diffusion under Node Attacks, 📝WWW, :octocat:Code
• Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks, 📝WWW
• Towards an Efficient and General Framework of Robust Training for Graph Neural Networks, 📝ICASSP
• Robust Graph Representation Learning via Neural Sparsification, 📝ICML
• Robust Training of Graph Convolutional Networks via Latent Perturbation, 📝ECML-PKDD
• Robust Collective Classification against Structural Attacks, 📝Preprint
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters, 📝CIKM, :octocat:Code
• Topological Effects on Attacks Against Vertex Classification, 📝arXiv
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning, 📝arXiv
• DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder, 📝arXiv, :octocat:Code
• Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning, 📝arXiv
• AANE: Anomaly Aware Network Embedding For Anomalous Link Detection, 📝ICDM
• Provably Robust Node Classification via Low-Pass Message Passing, 📝ICDM
• Graph-Revised Convolutional Network, 📝ECML-PKDD, :octocat:Code

2019

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• Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure, 📝TKDE, :octocat:Code
• Bayesian graph convolutional neural networks for semi-supervised classification, 📝AAAI, :octocat:Code
• Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations, 📝arXiv
• Examining Adversarial Learning against Graph-based IoT Malware Detection Systems, 📝arXiv
• Adversarial Embedding: A robust and elusive Steganography and Watermarking technique, 📝arXiv
• Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning, 📝arXiv, :octocat:Code
• Adversarial Defense Framework for Graph Neural Network, 📝arXiv
• GraphSAC: Detecting anomalies in large-scale graphs, 📝arXiv
• Edge Dithering for Robust Adaptive Graph Convolutional Networks, 📝arXiv
• Can Adversarial Network Attack be Defended?, 📝arXiv
• GraphDefense: Towards Robust Graph Convolutional Networks, 📝arXiv
• Adversarial Training Methods for Network Embedding, 📝WWW, :octocat:Code
• Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, 📝IJCAI, :octocat:Code
• Improving Robustness to Attacks Against Vertex Classification, 📝MLG@KDD
• Adversarial Robustness of Similarity-Based Link Prediction, 📝ICDM
• αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model, 📝CIKM
• Batch Virtual Adversarial Training for Graph Convolutional Networks, 📝ICML, :octocat:Code
• Latent Adversarial Training of Graph Convolution Networks, 📝LRGSD@ICML, :octocat:Code
• Characterizing Malicious Edges targeting on Graph Neural Networks, 📝ICLR OpenReview, :octocat:Code
• Comparing and Detecting Adversarial Attacks for Graph Deep Learning, 📝RLGM@ICLR
• Virtual Adversarial Training on Graph Convolutional Networks in Node Classification, 📝PRCV
• Robust Graph Convolutional Networks Against Adversarial Attacks, 📝KDD, :octocat:Code
• Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications, 📝NAACL, :octocat:Code
• Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective, 📝IJCAI, :octocat:Code
• Robust Graph Data Learning via Latent Graph Convolutional Representation, 📝arXiv

2018

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• Adversarial Personalized Ranking for Recommendation, 📝SIGIR, :octocat:Code

2017

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• Adversarial Sets for Regularising Neural Link Predictors, 📝UAI, :octocat:Code

🔐Certification

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• Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation, 📝KDD'2021, :octocat:Code
• Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks, 📝ICLR'2021, :octocat:Code
• Adversarial Immunization for Improving Certifiable Robustness on Graphs, 📝WSDM'2021
• Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning, 📝ICLR OpenReview'2021
• Robust Certification for Laplace Learning on Geometric Graphs, 📝MSML’2021
• Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning, 📝AAAI'2020
• Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks, 📝NeurIPS'2020, :octocat:Code
• Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing, 📝WWW'2020
• Efficient Robustness Certificates for Discrete Data: Sparsity - Aware Randomized Smoothing for Graphs, Images and More, 📝ICML'2020, :octocat:Code
• Abstract Interpretation based Robustness Certification for Graph Convolutional Networks, 📝ECAI'2020
• Certifiable Robustness of Graph Convolutional Networks under Structure Perturbation, 📝KDD'2020, :octocat:Code
• Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing, 📝GLOBECOM'2020
• Certifiable Robustness and Robust Training for Graph Convolutional Networks, 📝KDD'2019, :octocat:Code
• Certifiable Robustness to Graph Perturbations, 📝NeurIPS'2019, :octocat:Code

⚖Stability

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• When Do GNNs Work: Understanding and Improving Neighborhood Aggregation, 📝IJCAI Workshop'2019, :octocat:Code
• Stability Properties of Graph Neural Networks, 📝arXiv'2019
• Stability and Generalization of Graph Convolutional Neural Networks, 📝KDD'2019
• Graph and Graphon Neural Network Stability, 📝arXiv'2020
• On the Stability of Graph Convolutional Neural Networks under Edge Rewiring, 📝arXiv'2020
• Stability of Graph Neural Networks to Relative Perturbations, 📝ICASSP'2020
• Graph Neural Networks: Architectures, Stability and Transferability, 📝arXiv'2020
• Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method, 📝arXiv'2020
• Towards a Unified Framework for Fair and Stable Graph Representation Learning, 📝UAI'2021, :octocat:Code
• Training Stable Graph Neural Networks Through Constrained Learning, 📝arXiv'2021
• Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data, 📝arXiv'2021
• Stability of Graph Convolutional Neural Networks to Stochastic Perturbations, 📝arXiv'2021
• Stability and Generalization Capabilities of Message Passing Graph Neural Networks, 📝arXiv'2022
• On the Prediction Instability of Graph Neural Networks, 📝arXiv'2022

🚀Others

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• Perturbation Sensitivity of GNNs, 📝cs224w'2019
• FLAG: Adversarial Data Augmentation for Graph Neural Networks, 📝arXiv'2020, :octocat:Code
• Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning, 📝arXiv'2020
• Watermarking Graph Neural Networks by Random Graphs, 📝arXiv'2020
• Training Robust Graph Neural Network by Applying Lipschitz Constant Constraint, 📝CentraleSupélec'2020, :octocat:Code Generating Adversarial Examples with Graph Neural Networks, 📝UAI'2021
• SIGL: Securing Software Installations Through Deep Graph Learning, 📝USENIX'2021
• CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks, 📝arXiv'2021
• When Does Self-Supervision Help Graph Convolutional Networks?, 📝ICML'2020
• We Cannot Guarantee Safety: The Undecidability of Graph Neural Network Verification, arXiv'2022

📃Survey

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• Deep Graph Structure Learning for Robust Representations: A Survey, 📝arXiv'2021
• Adversarial Attacks and Defenses in Images, Graphs and Text: A Review, 📝arXiv'2019
• Deep Learning on Graphs: A Survey, 📝arXiv'2018
• Adversarial Attack and Defense on Graph Data: A Survey, 📝arXiv'2018
• A Survey of Adversarial Learning on Graph, 📝arXiv'2020
• Graph Neural Networks Taxonomy, Advances and Trends, 📝arXiv'2020
• Robustness of deep learning models on graphs: A survey, 📝AI Open'2021
• Graph Neural Networks Methods, Applications, and Opportunities, 📝arXiv'2021
• A Comparative Study on Robust Graph Neural Networks to Structural Noises, 📝AAAI DLG'2022
• Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies, 📝SIGKDD Explorations'2021
• Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack, 📝arXiv'2022
• Graph Vulnerability and Robustness: A Survey, 📝TKDE'2022
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability, arXiv'2022
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends, arXiv'2022
• A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection, arXiv'2022

⚙Toolbox

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• DeepRobust: a Platform for Adversarial Attacks and Defenses, 📝AAAI’2021, :octocat:DeepRobust
• GreatX: A graph reliability toolbox based on PyTorch and PyTorch Geometric, 📝arXiv’2022, :octocat:GreatX
• Evaluating Graph Vulnerability and Robustness using TIGER, 📝arXiv‘2021, :octocat:TIGER
• Graph Robustness Benchmark: Rethinking and Benchmarking Adversarial Robustness of Graph Neural Networks, 📝NeurIPS'2021, :octocat:Graph Robustness Benchmark (GRB)

🔗Resource

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• Awesome Adversarial Learning on Recommender System :octocat:Link
• Awesome Graph Attack and Defense Papers :octocat:Link
• Graph Adversarial Learning Literature :octocat:Link
• A Complete List of All (arXiv) Adversarial Example Papers 🌐Link
• Adversarial Attacks and Defenses Frontiers, Advances and Practice, KDD'20 tutorial, 🌐Link
• Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection, KDD'22 tutorial, 🌐Link